March 3rd, ’23

Overview

1 | background

2 | questions

3 | approaches

4 | methods

5 | results

6 | future

7 | conclusions

Team

  • Emily J. Woodworth (pollen morphology, and microscopy)
  • Jane Ogilvie (ecological fieldwork)
  • Sophie Taddeo (supervisory geospatial statistician)
  • Paul CaraDonna (big picture(s,) and little bees)
  • Jeremie Fant (all things molecular and tied together)

i.e. an in-house production.

the world is big - 1.1

… really really big - 1.2

funding opportunties - 1.3

haha made you look

sample the planet - 1.4

  • Bullet 1
  • Bullet 2
  • Bullet 3

Primary Roads

from and back to functional forms - 1.5

plant species in ecology - 1.6

  • mis-identification is very common
  • mis-identification can lead to nebulous understandings
  • mis-identification can lead to mis-management

Cirsium scariosum

insects species in ecology - 1.7

from organisms to interactions - 1.8

  • Bullet 1
  • Bullet 2
  • Bullet 3

Solidago spathulata & Megachile wheeleri, by A. Litz

pollen records - 1.9

metabarcoding - 1.10

old barcodes - 1.11

new barcodes? - 1.12

angiosperms 353 - 1.13

2 - questions

a353 as barcodes? - 2.1

serverless genomics? - 2.2

time and species rich clades - 2.3

are a353 semi-quantitative? - 2.4

are floral visitation and pollen records congruent? - 2.5

3 - approaches

Flora free data - 3.1

  • Bullet 1
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FPNW 2nd

Flora free data; space - 3.1.1

Flora free data; time - 3.1.2

custom sequence databases - 3.2

identify pollen grains - 3.3

queen bee pollen loads - 3.4

4 - methods

  • field work
  • spatial
  • temporal
  • morphologic
  • laboratory
  • bioinformatic
  • post-classification

study system & field work - 4.1

PICTURE OF RMBL

pollen morphological identification 4.2

workflow

pollen reference library 4.2.1

PICTURE OF POLLEN SLIDES FORM EMILY PRESENTATION

pollen corbiculae loads 4.2.2

  • Bullet 1
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Corbiculae Sample

molecular barcoding 4.3

spatial analysis 4.3.1

plant species for distribution modelling 4.3.1.1.

LINEAR REGRESSION IMAGE

species distribution modelling - 4.3.1.2

sdm evaluations - 4.3.1.2

  • in pipeline, True skill statistics

temporal modelling - 4.3.2

temporal modelling subset - 4.3.2.1

SPATIAL SUBSET PICTURE

temporal modelling distributions - 4.3.2.2

WEIBULL PICTURE ?

barcode references library - 4.4

genomics work - 4.4.1

  • Bullet 1
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workflow

plant genomic reference dna - 4.4.2

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Leaf Tissue from RMH

pollen genomics dna - 4.4.3

  • Bullet 1
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hyb-seq

barcoding informatics - 4.4.4

IMAGE OF COMPUTER BUILDING

metabarcoding - 4.5

sequence database generation - 4.5.1

  • Kew Tree of Life ~ ### taxa
  • US ~ xx TAXA

database

sequence assignment - 4.5.2

semi-quantitative evidence 4.5.3

5

results

field work

  • observations of Queen Bumble bees!
  • of plant unqiue plant species involved
  • corbiculae loads

corbiculae

species distribution modelling - 5.2

Logistic regression assessing accuracy of SDMs
Metric Value Metric Value
Accuracy (Training) 83.75 F-Score 0.84
Accuracy (Test) 84.00 AUC 0.92
Recall 81.03 Concordance 0.92
True Neg. Rate 86.97 Discordance 0.08
Precision 88.04 Tied 0.00

he 554 vascular plants with biotic pollination syndromes, the 493 ML ensembles accurately predicted the presence of 362 (65.3%), incorrectly predicted the presence of 64 (11.6%), incorrectly predicted 34 true presences (6.1%) as being absent, and correctly predicted the true absence of 33 (6.0%).

The balanced accuracy of the ensembled models is 0.627 (Sensitivity = 0.340, Specificity 0.914). Of the 554 vascular plants with biotic pollination syndromes, the 475 LM ensembles accurately predicted the presence of 286 (51.6%), incorrectly predicted the presence of 41 (14.3%), incorrectly predicted 93 true presences (16.8%) as being absent, and correctly predicted the true absence of 55 (9.9%). The balanced accuracy of the ensembled models is 0.664 (Sensitivity = 0.573, Specificity 0.754). Of the 554 vascular plants with biotic pollination syndromes in the flora 13 (2.3%) were in the Orchid family and 41 (7.4%) are non-natives, both of which are restricted from the database, and can only reduce the number of true predicted presences by roughly 10%.

sdm evaluations - 5.3

ml lm
ensembles 493 473
true + 362 286
true - 33 55
false + 64 41
false - 34 93
  • Plot Level, 117 species total (109 eligible for modelling…)
    • ML: 105 (89.7% (96.3%))
    • LM: 102 (87.2% (93.5%))

coarse phenological modelling - 5.4

  • strong agreement between first and peak flower periods
  • good agreement between last flower date
  • no agreement with duration! - species do not ‘line up’

flower dates

metabarcoding - 5.5

sequence database generation - 5.6

sequence assignment - 5.7

semi-quantitative evidence - 5.8

6

Discussion

7

future

new data sets?

7.1

  • artificial pollen loads
  • Gunnison Sage-Grouse scat

novel approaches

7.2

  • searching for variable loci
  • flanking regions

8

conclusions

promising

acknowledgements

two super fantastic technicians for the field seasons I worked during school, made my life SO easy
find them funding and recruit them, and then give me a finders fee.

  • Dani Yashinowitz B.S. (Yellowstone National Park, botanist & crew lead, Whitebark Pine Surveys (!!!))
  • Hannah Lovell B.S. (Telluride Mountain Resort, and ISO work)

acknowledgments

Employment: Yingying Xie, Josh Scholll, Sam Isham, Kelly McMillen, Kay Hajek, Linda Vance, Cassandra Owen, Ken Holsinger

Project: Nyree Zerega, Pat Herendeen, Hilary Noble, Zoe Diaz-Martinez, Angela McDonnell, Elena Loke, Ian Breckheimer, Ben Legler, Ernie Nelson, Charles (Rick) Williams, D. Knoke, L. Brummer, J. Boyd, C. Davidson, I. Gilman, M. Kirkpatrick, S. McCauley, J. Smith, K. Taylor, & C. Williams. David Giblin, Mare Nazaire, Sarah Burnett, Lauren Price, T.C.H. Cole. eliot Gardner.